Designing Pareto-optimal RAG workflows with syftr

a year ago

The article introduces syftr, an open-source framework designed to automate the identification of Pareto-optimal generative AI workflows, balancing accuracy, cost, and latency.
Syftr addresses the combinatorial explosion of choices in AI workflows by using multi-objective Bayesian Optimization to efficiently search through over 10²³ possible configurations.
The framework includes a novel early stopping mechanism called Pareto Pruner, which reduces computational cost and search time by halting evaluations of unpromising workflows.
Syftr is framework-agnostic and can be integrated with other tools like Trace, DSPy, and TextGrad for further optimization of prompts and workflow components.
A case study on the CRAG Sports benchmark demonstrates syftr's ability to identify workflows that maintain high accuracy while significantly reducing costs, with non-agentic workflows often dominating the Pareto frontier.
The article highlights the limitations of current model benchmarks, which fail to capture system-level performance when models are integrated into larger workflows.
Syftr is built on open-source technologies like Ray, Optuna, LlamaIndex, and HuggingFace Datasets, and it welcomes community contributions to extend its capabilities.
Future directions for syftr include meta-learning to accelerate searches, deeper integration with prompt optimization frameworks, and expansion into multi-agent and diverse task evaluations.

Hasty Briefsbeta